Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data

Object-based image analysis and machine-learning classification were applied to multispectral camera array data acquired by a small rotating blade unmanned aerial vehicle (UAV) over a hardwood forest in eastern Ontario. White birch, aspen, and two species of maple were surveyed in the field. Images were segmented and the resulting objects were visually confirmed to correspond with the sampled tree crowns. Following the application of machine-learning classification using the Random Forest algorithm, an independent validation sample of 23 tree crowns was, overall, approximately 78% correct. Aspen and birch were the most distinct species; the two maples appeared to be confused with each other and with immature trees and understory shrubs. Classification accuracy, commission errors, and variable importance were interpreted to be consistent with experience documented in aerial photointerpretation selection and elimination keys for northern hardwoods. Additional tests are recommended to more fully analyse the accuracy of deciduous tree species classification using digital analysis of high spatial resolution multispectral UAV imagery.